Abstract
Autonomous object counting system can help industries to keep track of their inventory in real time and adjust their production rate suitably. In this paper we have proposed a robust algorithm which is capable of detecting all the instances of a particular object in a scene image and report their count. The algorithm starts by intelligently selecting Speeded Up Robust Feature (SURF) points on the basis stability and proximity in the prototype image, i.e. the image of the object to be counted. SURF points on the scene image are detected and matched to the ones on the prototype image. The notion of Feature Grid Vector (FGV) and Feature Grid Cluster (FGC) is introduced to group SURF points lying on a particular instance of the prototype. A learning model based on Support Vector Machine has been developed to separate out the true instances of the prototype from the false alarms. Both the training and inference occur almost in real time for all practical purposes. The algorithm is robust to illumination variations in the scene image and is capable of detecting instances of the prototype having different distance and orientation w.r.t. the camera. The complete algorithm has been embodied into a desktop application, which uses a camera feed to report the real time count of the prototype in the scene image.
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Verma, N.K., Goyal, A., Vardhan, A.H., Sevakula, R.K., Salour, A. (2016). Object Matching Using Speeded Up Robust Features. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_34
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DOI: https://doi.org/10.1007/978-3-319-27000-5_34
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